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fixed bugs for tf1.0 #22

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fixed bugs for tf1.0 #22

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2 changes: 1 addition & 1 deletion model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,4 +85,4 @@ def conv2d(x, W, stride):
W_fc5 = weight_variable([10, 1])
b_fc5 = bias_variable([1])

y = tf.mul(tf.atan(tf.matmul(h_fc4_drop, W_fc5) + b_fc5), 2) #scale the atan output
y = tf.multiply(tf.atan(tf.matmul(h_fc4_drop, W_fc5) + b_fc5), 2) #scale the atan output
10 changes: 6 additions & 4 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,20 +12,22 @@

train_vars = tf.trainable_variables()

loss = tf.reduce_mean(tf.square(tf.sub(model.y_, model.y))) + tf.add_n([tf.nn.l2_loss(v) for v in train_vars]) * L2NormConst
loss = tf.reduce_mean(tf.square(tf.subtract(model.y_, model.y))) + tf.add_n([tf.nn.l2_loss(v) for v in train_vars]) * L2NormConst
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
sess.run(tf.initialize_all_variables())

# create a summary to monitor cost tensor
tf.scalar_summary("loss", loss)
tf.summary.scalar("loss", loss)
# merge all summaries into a single op
merged_summary_op = tf.merge_all_summaries()
merged_summary_op = tf.summary.merge_all()


saver = tf.train.Saver(write_version = saver_pb2.SaverDef.V1)

# op to write logs to Tensorboard
logs_path = './logs'
summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())


epochs = 30
batch_size = 100
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